Overview

Dataset statistics

Number of variables13
Number of observations898
Missing cells429
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.3 KiB
Average record size in memory104.1 B

Variable types

Categorical7
Numeric6

Warnings

english_name has a high cardinality: 898 distinct values High cardinality
defense is highly correlated with sp_defenseHigh correlation
sp_defense is highly correlated with defenseHigh correlation
hp is highly correlated with attackHigh correlation
attack is highly correlated with hp and 1 other fieldsHigh correlation
defense is highly correlated with attack and 1 other fieldsHigh correlation
sp_attack is highly correlated with sp_defenseHigh correlation
sp_defense is highly correlated with defense and 1 other fieldsHigh correlation
secondary_type is highly correlated with primary_type and 1 other fieldsHigh correlation
sp_defense is highly correlated with defenseHigh correlation
sp_attack is highly correlated with attackHigh correlation
attack is highly correlated with sp_attack and 2 other fieldsHigh correlation
primary_type is highly correlated with secondary_typeHigh correlation
defense is highly correlated with sp_defense and 2 other fieldsHigh correlation
hp is highly correlated with attack and 1 other fieldsHigh correlation
gen is highly correlated with secondary_typeHigh correlation
secondary_type has 429 (47.8%) missing values Missing
english_name is uniformly distributed Uniform
english_name has unique values Unique

Reproduction

Analysis started2021-08-08 20:44:58.714717
Analysis finished2021-08-08 20:45:02.107506
Duration3.39 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

english_name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct898
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Dedenne
 
1
Xerneas
 
1
Krokorok
 
1
Clobbopus
 
1
Skrelp
 
1
Other values (893)
893 

Length

Max length12
Median length7
Mean length7.532293987
Min length3

Characters and Unicode

Total characters6764
Distinct characters61
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique898 ?
Unique (%)100.0%

Sample

1st rowBulbasaur
2nd rowIvysaur
3rd rowVenusaur
4th rowCharmander
5th rowCharmeleon

Common Values

ValueCountFrequency (%)
Dedenne1
 
0.1%
Xerneas1
 
0.1%
Krokorok1
 
0.1%
Clobbopus1
 
0.1%
Skrelp1
 
0.1%
Scrafty1
 
0.1%
Garchomp1
 
0.1%
Electabuzz1
 
0.1%
Stantler1
 
0.1%
Palossand1
 
0.1%
Other values (888)888
98.9%

Length

2021-08-08T22:45:02.191681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tapu4
 
0.4%
mr2
 
0.2%
mime2
 
0.2%
lucario1
 
0.1%
swampert1
 
0.1%
spectrier1
 
0.1%
staryu1
 
0.1%
kricketune1
 
0.1%
litleo1
 
0.1%
wigglytuff1
 
0.1%
Other values (891)891
98.3%

Most occurring characters

ValueCountFrequency (%)
a613
 
9.1%
e581
 
8.6%
o556
 
8.2%
r499
 
7.4%
i483
 
7.1%
l397
 
5.9%
n383
 
5.7%
t317
 
4.7%
u273
 
4.0%
s221
 
3.3%
Other values (51)2441
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5834
86.3%
Uppercase Letter908
 
13.4%
Space Separator8
 
0.1%
Other Punctuation6
 
0.1%
Dash Punctuation5
 
0.1%
Other Symbol2
 
< 0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a613
10.5%
e581
 
10.0%
o556
 
9.5%
r499
 
8.6%
i483
 
8.3%
l397
 
6.8%
n383
 
6.6%
t317
 
5.4%
u273
 
4.7%
s221
 
3.8%
Other values (17)1511
25.9%
Uppercase Letter
ValueCountFrequency (%)
S123
13.5%
C76
 
8.4%
M74
 
8.1%
P59
 
6.5%
G57
 
6.3%
D56
 
6.2%
T53
 
5.8%
B49
 
5.4%
A40
 
4.4%
L39
 
4.3%
Other values (16)282
31.1%
Other Punctuation
ValueCountFrequency (%)
.3
50.0%
'2
33.3%
:1
 
16.7%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Decimal Number
ValueCountFrequency (%)
21
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6742
99.7%
Common22
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a613
 
9.1%
e581
 
8.6%
o556
 
8.2%
r499
 
7.4%
i483
 
7.2%
l397
 
5.9%
n383
 
5.7%
t317
 
4.7%
u273
 
4.0%
s221
 
3.3%
Other values (43)2419
35.9%
Common
ValueCountFrequency (%)
8
36.4%
-5
22.7%
.3
 
13.6%
'2
 
9.1%
1
 
4.5%
1
 
4.5%
21
 
4.5%
:1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6760
99.9%
Misc Symbols2
 
< 0.1%
Latin 1 Sup2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a613
 
9.1%
e581
 
8.6%
o556
 
8.2%
r499
 
7.4%
i483
 
7.1%
l397
 
5.9%
n383
 
5.7%
t317
 
4.7%
u273
 
4.0%
s221
 
3.3%
Other values (48)2437
36.1%
Misc Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Latin 1 Sup
ValueCountFrequency (%)
é2
100.0%

gen
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
V
156 
I
151 
III
135 
IV
107 
II
100 
Other values (3)
249 

Length

Max length4
Median length2
Mean length2.10467706
Min length1

Characters and Unicode

Total characters1890
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowI
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
V156
17.4%
I151
16.8%
III135
15.0%
IV107
11.9%
II100
11.1%
VIII89
9.9%
VII88
9.8%
VI72
8.0%

Length

2021-08-08T22:45:02.281270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T22:45:02.319257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v156
17.4%
i151
16.8%
iii135
15.0%
iv107
11.9%
ii100
11.1%
viii89
9.9%
vii88
9.8%
vi72
8.0%

Most occurring characters

ValueCountFrequency (%)
I1378
72.9%
V512
 
27.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1890
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I1378
72.9%
V512
 
27.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I1378
72.9%
V512
 
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I1378
72.9%
V512
 
27.1%

primary_type
Categorical

HIGH CORRELATION

Distinct18
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
water
123 
normal
109 
grass
86 
bug
75 
fire
58 
Other values (13)
447 

Length

Max length8
Median length5
Mean length5.265033408
Min length3

Characters and Unicode

Total characters4728
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrass
2nd rowgrass
3rd rowgrass
4th rowfire
5th rowfire

Common Values

ValueCountFrequency (%)
water123
13.7%
normal109
12.1%
grass86
 
9.6%
bug75
 
8.4%
fire58
 
6.5%
psychic58
 
6.5%
rock50
 
5.6%
electric49
 
5.5%
fighting36
 
4.0%
dark36
 
4.0%
Other values (8)218
24.3%

Length

2021-08-08T22:45:02.405041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
water123
13.7%
normal109
12.1%
grass86
 
9.6%
bug75
 
8.4%
fire58
 
6.5%
psychic58
 
6.5%
rock50
 
5.6%
electric49
 
5.5%
fighting36
 
4.0%
dark36
 
4.0%
Other values (8)218
24.3%

Most occurring characters

ValueCountFrequency (%)
r598
12.6%
a406
 
8.6%
e367
 
7.8%
g337
 
7.1%
i328
 
6.9%
s326
 
6.9%
o326
 
6.9%
c292
 
6.2%
t269
 
5.7%
n253
 
5.4%
Other values (11)1226
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4728
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r598
12.6%
a406
 
8.6%
e367
 
7.8%
g337
 
7.1%
i328
 
6.9%
s326
 
6.9%
o326
 
6.9%
c292
 
6.2%
t269
 
5.7%
n253
 
5.4%
Other values (11)1226
25.9%

Most occurring scripts

ValueCountFrequency (%)
Latin4728
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r598
12.6%
a406
 
8.6%
e367
 
7.8%
g337
 
7.1%
i328
 
6.9%
s326
 
6.9%
o326
 
6.9%
c292
 
6.2%
t269
 
5.7%
n253
 
5.4%
Other values (11)1226
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r598
12.6%
a406
 
8.6%
e367
 
7.8%
g337
 
7.1%
i328
 
6.9%
s326
 
6.9%
o326
 
6.9%
c292
 
6.2%
t269
 
5.7%
n253
 
5.4%
Other values (11)1226
25.9%

secondary_type
Categorical

HIGH CORRELATION
MISSING

Distinct18
Distinct (%)3.8%
Missing429
Missing (%)47.8%
Memory size7.1 KiB
flying
95 
poison
37 
psychic
35 
ground
35 
fairy
34 
Other values (13)
233 

Length

Max length8
Median length6
Mean length5.560767591
Min length3

Characters and Unicode

Total characters2608
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpoison
2nd rowpoison
3rd rowpoison
4th rowflying
5th rowflying

Common Values

ValueCountFrequency (%)
flying95
 
10.6%
poison37
 
4.1%
psychic35
 
3.9%
ground35
 
3.9%
fairy34
 
3.8%
fighting26
 
2.9%
dragon25
 
2.8%
dark25
 
2.8%
steel24
 
2.7%
grass22
 
2.4%
Other values (8)111
 
12.4%
(Missing)429
47.8%

Length

2021-08-08T22:45:02.487677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flying95
20.3%
poison37
 
7.9%
psychic35
 
7.5%
ground35
 
7.5%
fairy34
 
7.2%
fighting26
 
5.5%
dark25
 
5.3%
dragon25
 
5.3%
steel24
 
5.1%
grass22
 
4.7%
Other values (8)111
23.7%

Most occurring characters

ValueCountFrequency (%)
i296
11.3%
g258
 
9.9%
n224
 
8.6%
r205
 
7.9%
o175
 
6.7%
f171
 
6.6%
y164
 
6.3%
s160
 
6.1%
l134
 
5.1%
a130
 
5.0%
Other values (11)691
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2608
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i296
11.3%
g258
 
9.9%
n224
 
8.6%
r205
 
7.9%
o175
 
6.7%
f171
 
6.6%
y164
 
6.3%
s160
 
6.1%
l134
 
5.1%
a130
 
5.0%
Other values (11)691
26.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2608
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i296
11.3%
g258
 
9.9%
n224
 
8.6%
r205
 
7.9%
o175
 
6.7%
f171
 
6.6%
y164
 
6.3%
s160
 
6.1%
l134
 
5.1%
a130
 
5.0%
Other values (11)691
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i296
11.3%
g258
 
9.9%
n224
 
8.6%
r205
 
7.9%
o175
 
6.7%
f171
 
6.6%
y164
 
6.3%
s160
 
6.1%
l134
 
5.1%
a130
 
5.0%
Other values (11)691
26.5%

hp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct102
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.0311804
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-08T22:45:02.532933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q150
median65
Q380
95-th percentile110
Maximum255
Range254
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.21370723
Coefficient of variation (CV)0.3797372011
Kurtosis7.477145754
Mean69.0311804
Median Absolute Deviation (MAD)15
Skewness1.688001125
Sum61990
Variance687.158447
MonotonicityNot monotonic
2021-08-08T22:45:02.587238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6074
 
8.2%
7064
 
7.1%
5061
 
6.8%
6550
 
5.6%
4046
 
5.1%
7546
 
5.1%
4544
 
4.9%
8043
 
4.8%
5538
 
4.2%
10035
 
3.9%
Other values (92)397
44.2%
ValueCountFrequency (%)
11
 
0.1%
101
 
0.1%
206
 
0.7%
254
 
0.4%
282
 
0.2%
3015
1.7%
311
 
0.1%
3516
1.8%
361
 
0.1%
371
 
0.1%
ValueCountFrequency (%)
2551
 
0.1%
2501
 
0.1%
2231
 
0.1%
2001
 
0.1%
1901
 
0.1%
1701
 
0.1%
1651
 
0.1%
1601
 
0.1%
1503
0.3%
1441
 
0.1%

attack
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.54454343
Minimum5
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-08T22:45:02.637890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q155
median75
Q395
95-th percentile130
Maximum181
Range176
Interquartile range (IQR)40

Descriptive statistics

Standard deviation29.66555905
Coefficient of variation (CV)0.3875594226
Kurtosis-0.345890652
Mean76.54454343
Median Absolute Deviation (MAD)20
Skewness0.2935451874
Sum68737
Variance880.0453938
MonotonicityNot monotonic
2021-08-08T22:45:02.689424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10042
 
4.7%
8041
 
4.6%
6541
 
4.6%
6041
 
4.6%
8538
 
4.2%
5037
 
4.1%
9037
 
4.1%
7536
 
4.0%
5536
 
4.0%
7034
 
3.8%
Other values (102)515
57.3%
ValueCountFrequency (%)
52
 
0.2%
103
 
0.3%
151
 
0.1%
2010
1.1%
221
 
0.1%
231
 
0.1%
241
 
0.1%
258
0.9%
271
 
0.1%
281
 
0.1%
ValueCountFrequency (%)
1811
 
0.1%
1651
 
0.1%
1602
0.2%
1504
0.4%
1471
 
0.1%
1451
 
0.1%
1431
 
0.1%
1404
0.4%
1391
 
0.1%
1372
0.2%

defense
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct107
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.88641425
Minimum5
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-08T22:45:02.741438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q150
median67
Q390
95-th percentile129.15
Maximum230
Range225
Interquartile range (IQR)40

Descriptive statistics

Standard deviation29.53618445
Coefficient of variation (CV)0.410872969
Kurtosis2.167346151
Mean71.88641425
Median Absolute Deviation (MAD)18
Skewness1.047886684
Sum64554
Variance872.386192
MonotonicityNot monotonic
2021-08-08T22:45:02.790587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5060
 
6.7%
6055
 
6.1%
7052
 
5.8%
8045
 
5.0%
4044
 
4.9%
6543
 
4.8%
9041
 
4.6%
5538
 
4.2%
4537
 
4.1%
10035
 
3.9%
Other values (97)448
49.9%
ValueCountFrequency (%)
52
 
0.2%
101
 
0.1%
154
 
0.4%
205
 
0.6%
231
 
0.1%
252
 
0.2%
282
 
0.2%
3017
1.9%
311
 
0.1%
322
 
0.2%
ValueCountFrequency (%)
2301
 
0.1%
2111
 
0.1%
2002
0.2%
1841
 
0.1%
1802
0.2%
1681
 
0.1%
1601
 
0.1%
1521
 
0.1%
1504
0.4%
1453
0.3%

sp_attack
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct105
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.68151448
Minimum10
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-08T22:45:02.843294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q146.25
median65
Q390
95-th percentile125
Maximum173
Range163
Interquartile range (IQR)43.75

Descriptive statistics

Standard deviation29.37260938
Coefficient of variation (CV)0.4215265641
Kurtosis-0.1623685193
Mean69.68151448
Median Absolute Deviation (MAD)20
Skewness0.5730536741
Sum62574
Variance862.7501819
MonotonicityNot monotonic
2021-08-08T22:45:02.894247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4063
 
7.0%
6058
 
6.5%
5049
 
5.5%
6547
 
5.2%
5545
 
5.0%
4537
 
4.1%
7036
 
4.0%
8036
 
4.0%
10035
 
3.9%
9533
 
3.7%
Other values (95)459
51.1%
ValueCountFrequency (%)
103
 
0.3%
153
 
0.3%
2010
 
1.1%
231
 
0.1%
242
 
0.2%
2513
1.4%
272
 
0.2%
293
 
0.3%
3029
3.2%
311
 
0.1%
ValueCountFrequency (%)
1731
 
0.1%
1541
 
0.1%
1511
 
0.1%
1507
0.8%
1455
0.6%
1372
 
0.2%
1361
 
0.1%
1355
0.6%
1341
 
0.1%
1312
 
0.2%

sp_defense
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.87639198
Minimum20
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-08T22:45:02.949116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30.85
Q150
median65
Q385
95-th percentile120
Maximum230
Range210
Interquartile range (IQR)35

Descriptive statistics

Standard deviation27.01214227
Coefficient of variation (CV)0.3865703638
Kurtosis1.724817562
Mean69.87639198
Median Absolute Deviation (MAD)17
Skewness0.8834179979
Sum62749
Variance729.65583
MonotonicityNot monotonic
2021-08-08T22:45:02.996360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5064
 
7.1%
6053
 
5.9%
7050
 
5.6%
8049
 
5.5%
5548
 
5.3%
6545
 
5.0%
7545
 
5.0%
9041
 
4.6%
4540
 
4.5%
4039
 
4.3%
Other values (89)424
47.2%
ValueCountFrequency (%)
206
 
0.7%
231
 
0.1%
2512
1.3%
3026
2.9%
313
 
0.3%
321
 
0.1%
331
 
0.1%
341
 
0.1%
3523
2.6%
361
 
0.1%
ValueCountFrequency (%)
2301
 
0.1%
2001
 
0.1%
1543
0.3%
1505
0.6%
1421
 
0.1%
1403
0.3%
1381
 
0.1%
1353
0.3%
1321
 
0.1%
1312
 
0.2%

speed
Real number (ℝ≥0)

Distinct117
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.94988864
Minimum5
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-08T22:45:03.121407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q145
median65
Q385
95-th percentile115
Maximum200
Range195
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.45659616
Coefficient of variation (CV)0.4314881609
Kurtosis0.09212427274
Mean65.94988864
Median Absolute Deviation (MAD)20
Skewness0.4437242026
Sum59223
Variance809.7778651
MonotonicityNot monotonic
2021-08-08T22:45:03.172299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6051
 
5.7%
5051
 
5.7%
6544
 
4.9%
7043
 
4.8%
3041
 
4.6%
4038
 
4.2%
4536
 
4.0%
8036
 
4.0%
9032
 
3.6%
8532
 
3.6%
Other values (107)494
55.0%
ValueCountFrequency (%)
53
 
0.3%
104
 
0.4%
131
 
0.1%
1512
1.3%
2016
1.8%
221
 
0.1%
234
 
0.4%
241
 
0.1%
2511
1.2%
261
 
0.1%
ValueCountFrequency (%)
2001
 
0.1%
1601
 
0.1%
1511
 
0.1%
1502
 
0.2%
1451
 
0.1%
1431
 
0.1%
1421
 
0.1%
1382
 
0.2%
1361
 
0.1%
1307
0.8%

is_legendary
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
878 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Length

2021-08-08T22:45:03.257135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T22:45:03.281409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

is_sublegendary
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
853 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0853
95.0%
145
 
5.0%

Length

2021-08-08T22:45:03.346031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T22:45:03.369458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0853
95.0%
145
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0853
95.0%
145
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0853
95.0%
145
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0853
95.0%
145
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0853
95.0%
145
 
5.0%

is_mythical
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
878 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Length

2021-08-08T22:45:03.429936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T22:45:03.453773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0878
97.8%
120
 
2.2%

Interactions

2021-08-08T22:45:00.044186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.094519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.139704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.181889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.227448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.269491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.314957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.365712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.412949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.515231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.563602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.608047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.655825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.699777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.742923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.783571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.827998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.868359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.912887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:00.959591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.010105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.057779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.110164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.155802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.205127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.245540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.287758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.329015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.373311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.420473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.477655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.525271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.574017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.625953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.681429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T22:45:01.726837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-08T22:45:03.482568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-08T22:45:03.550495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-08T22:45:03.613158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-08T22:45:03.675810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-08T22:45:03.733803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-08T22:45:01.880215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-08T22:45:01.973341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-08T22:45:02.032073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

english_namegenprimary_typesecondary_typehpattackdefensesp_attacksp_defensespeedis_legendaryis_sublegendaryis_mythical
0BulbasaurIgrasspoison454949656545000
1IvysaurIgrasspoison606263808060000
2VenusaurIgrasspoison80828310010080000
3CharmanderIfireNaN395243605065000
4CharmeleonIfireNaN586458806580000
5CharizardIfireflying78847810985100000
6SquirtleIwaterNaN444865506443000
7WartortleIwaterNaN596380658058000
8BlastoiseIwaterNaN79831008510578000
9CaterpieIbugNaN453035202045000

Last rows

english_namegenprimary_typesecondary_typehpattackdefensesp_attacksp_defensespeedis_legendaryis_sublegendaryis_mythical
888ZamazentaVIIIfightingfighting9213011580115138000
889EternatusVIIIpoisondragon140859514595130000
890KubfuVIIIfightingNaN609060535072010
891UrshifuVIIIfightingdark100130100636097010
892ZarudeVIIIdarkgrass1051201057095105000
893RegielekiVIIIelectricNaN801005010050200010
894RegidragoVIIIdragonNaN200100501005080010
895GlastrierVIIIiceNaN1001451306511030010
896SpectrierVIIIghostNaN100656014580130000
897CalyrexVIIIpsychicgrass1008080808080000